In this exercise, you will load a filtered gapminder
dataset - with a subset of data on global development from 1952 - 2007
in increments of 5 years - to capture the period between the Second
World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks within this script.
First, start with installing and activating the relevant packages
tidyverse, gganimate, and
gapminder if you do not have them already. Pay
attention to what warning messages you get when installing
gganimate, as your computer might need other packages than
gifski and av
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Warning: pakke 'gganimate' blev bygget under R version 4.4.3
## Warning: pakke 'gifski' blev bygget under R version 4.4.3
## Warning: pakke 'av' blev bygget under R version 4.4.3
## Warning: pakke 'gapminder' blev bygget under R version 4.4.3
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw())
options(scipen = 999)
ggplot(data = subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point(aes(colour = continent)) +
scale_x_log10() +
ggtitle("1952")
Question 1: why does it make sense to have a log10 scale
(
scale_x_log10()) on the x axis? (hint: try to comment
it out and observe the result)
-It spreads out the data, so it makes it easier to read.
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(data = subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point(aes (colour = continent)) +
scale_x_log10() +
ggtitle("2007")
Question 2: In Figure 1: Who is the outlier (the richest country in
1952) far right on the x axis?
The richest country in 1952 is Kuwait. I used the code below to find
it.
gapminder %>%
filter(year == 1952) %>%
slice_max(gdpPercap, n = 1)
## # A tibble: 1 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Kuwait Asia 1952 55.6 160000 108382.
Question 3: Figures 1 and 2: Differentiate the
continents by color, and fix the axis labels and units
to be more legible (Hint: the 2.50e+08 is so called
“scientific notation”. You want to eliminate it.)
- I changed it to colour in the code that was already written here above
the the visualisation. I used the function options(scipen = 999) to
change to numbers to natural numbers, which makes it easier to read.
Question 4: What are the five richest countries in the world in
2007?
Norway, Kuwait, Singapore, United States and Ireland
gapminder %>%
filter(year == 2007) %>%
slice_max(gdpPercap, n = 5)
## # A tibble: 5 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Norway Europe 2007 80.2 4627926 49357.
## 2 Kuwait Asia 2007 77.6 2505559 47307.
## 3 Singapore Asia 2007 80.0 4553009 47143.
## 4 United States Americas 2007 78.2 301139947 42952.
## 5 Ireland Europe 2007 78.9 4109086 40676.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
**This plot collates all the points across time. The next step is to split it into years and animate it.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
### Option 2 Animate using transition_time() This option smooths the
transition between different ‘frames’, because it interpolates and adds
transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Question 5: Can you add a title to one or both of the animations
above that will change in sync with the animation?
(Hint: search labeling for
transition_states() and transition_time()
functions respectively)
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point(alpha = 0.7) +
scale_x_log10() +
transition_time(year) +
labs(title = "Global Development from 1952 to 2007. Year:{frame_time}",
x = "GDP per capita in 2005 USD",
y = "Life Expectancy")
anim2
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = continent)) +
geom_point(alpha = 0.7) +
scale_x_log10() +
transition_time(year) +
labs(title = "Global Development from 1952 to 2007. Year:{frame_time}",
x = "GDP per capita in 2005 USD",
y = "Life Expectancy") +
theme(
plot.title = element_text(size=18, face="bold"),
axis.title.x = element_text(size=16, face="bold"),
axis.title.y = element_text(size=16, face="bold"),
axis.text.x = element_text(size=14),
axis.text.y = element_text(size=14))
anim2
#I used chatgpt to find the theme to make the texts larger and therefore more readable.
gapminder_unfiltered dataset or
download more historical data at https://www.gapminder.org/data/ ]birth_year <- 2002
Difference <- gapminder %>%
select(lifeExp, year, continent) %>%
filter(year == c(2002, 2007)) %>%
ggplot(aes(x = continent, y = lifeExp, fill = continent )) +
geom_boxplot()+
facet_wrap(~year)+
labs(title = "Difference In Life Expectancy From 2002 To 2007",
y = "Life Expectancy")
Difference
*Is the world a better place today than the year you were born?
- Using the data from gapminder on life expectancy in the year 2002 and
2007 there is a slight but significant increase in the overall life
expectancy on all continents. Life expectancy has been chosen as the
variable for answering the question; “is the world a better place
today”. This choice was made due to the increase in life expectancy
showing the result of an increase in better health, fewer child death,
less war, and a better standard of living in average for all peoples.
Since the boxplot shows an increase in this variable, then we can
conclude that the world in fact is better place based on this one
variable alone. However, the use of only one variable, to answer
question as complex as this, should not be considered as a clear and
defining answer. And should therefore be compared to other measurements
to insure the data’s reliability and stable growth and increase in
standard of living across the board. We could have chosen any other of
the variables in the dataset and it would result in the same conclusion.
However, the illustration shows a clear increase in life expectancy over
a period of just five years, especially in Africa and Asia, the
countries with the lowest values, where we see a larger increase. This
is a sign that the world is developing into a better place for all.